scholarly journals COMBINING SPECTRAL AND TEXTURE FEATURES USING RANDOM FOREST ALGORITHM: EXTRACTING IMPERVIOUS SURFACE AREA IN WUHAN

2016 ◽  
Vol XLI-B7 ◽  
pp. 351-358
Author(s):  
Zhenfeng Shao ◽  
Yuan Zhang ◽  
Lei Zhang ◽  
Yang Song ◽  
Minjun Peng
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Trees ◽  
Surface Area ◽  
Random Forests ◽  
Impervious Surface ◽  
Impervious Surfaces ◽  
Remote Sensing Images ◽  
Impervious Surface Area

Impervious surface area (ISA) is one of the most important indicators of urban environments. At present, based on multi-resolution remote sensing images, numerous approaches have been proposed to extract impervious surface, using statistical estimation, sub-pixel classification and spectral mixture analysis method of sub-pixel analysis. Through these methods, impervious surfaces can be effectively applied to regional-scale planning and management. However, for the large scale region, high resolution remote sensing images can provide more details, and therefore they will be more conducive to analysis environmental monitoring and urban management. Since the purpose of this study is to map impervious surfaces more effectively, three classification algorithms (random forests, decision trees, and artificial neural networks) were tested for their ability to map impervious surface. Random forests outperformed the decision trees, and artificial neural networks in precision. Combining the spectral indices and texture, random forests is applied to impervious surface extraction with a producer’s accuracy of 0.98, a user’s accuracy of 0.97, and an overall accuracy of 0.98 and a kappa coefficient of 0.97.

scholarly journals COMBINING SPECTRAL AND TEXTURE FEATURES USING RANDOM FOREST ALGORITHM: EXTRACTING IMPERVIOUS SURFACE AREA IN WUHAN

2016 ◽  
Vol XLI-B7 ◽  
pp. 351-358 ◽  
Author(s):  
Zhenfeng Shao ◽  
Yuan Zhang ◽  
Lei Zhang ◽  
Yang Song ◽  
Minjun Peng
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Trees ◽  
Surface Area ◽  
Random Forests ◽  
Impervious Surface ◽  
Impervious Surfaces ◽  
Remote Sensing Images ◽  
Impervious Surface Area

Impervious surface area (ISA) is one of the most important indicators of urban environments. At present, based on multi-resolution remote sensing images, numerous approaches have been proposed to extract impervious surface, using statistical estimation, sub-pixel classification and spectral mixture analysis method of sub-pixel analysis. Through these methods, impervious surfaces can be effectively applied to regional-scale planning and management. However, for the large scale region, high resolution remote sensing images can provide more details, and therefore they will be more conducive to analysis environmental monitoring and urban management. Since the purpose of this study is to map impervious surfaces more effectively, three classification algorithms (random forests, decision trees, and artificial neural networks) were tested for their ability to map impervious surface. Random forests outperformed the decision trees, and artificial neural networks in precision. Combining the spectral indices and texture, random forests is applied to impervious surface extraction with a producer’s accuracy of 0.98, a user’s accuracy of 0.97, and an overall accuracy of 0.98 and a kappa coefficient of 0.97.

scholarly journals VEGETATION COVERAGE AND IMPERVIOUS SURFACE AREA ESTIMATED BASED ON THE ESTARFM MODEL AND REMOTE SENSING MONITORING

2018 ◽  
Vol IV-3 ◽  
pp. 113-119
Author(s):  
Rongming Hu ◽  
Shu Wang ◽  
Jiao Guo ◽  
Liankun Guo
Keyword(s):  
Remote Sensing ◽  
Surface Area ◽  
Temporal Resolution ◽  
Remote Sensing Data ◽  
Impervious Surface ◽  
Vegetation Coverage ◽  
Landsat 8 ◽  
Spatiotemporal Resolution ◽  
Impervious Surface Area ◽  
Sensing Data

Impervious surface area and vegetation coverage are important biophysical indicators of urban surface features which can be derived from medium-resolution images. However, remote sensing data obtained by a single sensor are easily affected by many factors such as weather conditions, and the spatial and temporal resolution can not meet the needs for soil erosion estimation. Therefore, the integrated multi-source remote sensing data are needed to carry out high spatio-temporal resolution vegetation coverage estimation. Two spatial and temporal vegetation coverage data and impervious data were obtained from MODIS and Landsat 8 remote sensing images. Based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), the vegetation coverage data of two scales were fused and the data of vegetation coverage fusion (ESTARFM FVC) and impervious layer with high spatiotemporal resolution (30 m, 8 day) were obtained. On this basis, the spatial variability of the seepage-free surface and the vegetation cover landscape in the study area was measured by means of statistics and spatial autocorrelation analysis. The results showed that: 1) ESTARFM FVC and impermeable surface have higher accuracy and can characterize the characteristics of the biophysical components covered by the earth's surface; 2) The average impervious surface proportion and the spatial configuration of each area are different, which are affected by natural conditions and urbanization. In the urban area of Xi'an, which has typical characteristics of spontaneous urbanization, landscapes are fragmented and have less spatial dependence.

scholarly journals The Impacts of the Expansion of Urban Impervious Surfaces on Urban Heat Islands in a Coastal City in China

2020 ◽  
Vol 12 (2) ◽  
pp. 475 ◽  
Author(s):  
Lizhong Hua ◽  
Xinxin Zhang ◽  
Qin Nie ◽  
Fengqin Sun ◽  
Lina Tang
Keyword(s):  
Surface Area ◽  
Fold Increase ◽  
Impervious Surface ◽  
Urban Heat Islands ◽  
Vegetation Coverage ◽  
Impervious Surfaces ◽  
Impervious Surface Area ◽  
Heat Islands ◽  
Urban Heat ◽  
Urban Impervious Surfaces

The effect of the expansion of urban impervious surfaces on surface urban heat islands (UHIs) has attracted research attention due to its relevance for studies of local climatic change and habitat comfort. In this study, using five satellite images of Xiamen city, Southeast China (four images from the Landsat 5 Thematic Mapper (TM) and one from the Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS)) acquired in summer between 1989 and 2016, together with spatial statistical methods, the changes in impervious surface area (ISA) were investigated, the spatiotemporal variation of the intensity of urban heat islands (UHIs) was explored, and the relationships between land surface temperature (LST) and the percentage of impervious surface area (ISA%), the normalized difference vegetation index (NDVI), and fractional vegetation coverage (Fv) were investigated. The results showed the following: (1) According to the biophysical composition index (BCI) combined with an ISA post-processing method, Xiamen has witnessed a substantial increase in ISA, showing a 6.1-fold increase from 1989 to 2016. The direction of ISA expansion was consistent throughout the study period in each of the five districts of Xiamen; (2) a bay-like UHI form is observed in the study area, which is remarkably distinct from the central-radial UHI form observed in previous studies of other cities; (3) the extent of UHIs in Xiamen greatly increased between 1989 and 2016, experiencing a 4.7-fold increase in UHI areas during this time. However, during the same period, the urban heat island ratio index (URI)—that is, the ratio of UHI area to ISA—decreased slightly. The UHI area decreased in some urban parts of Xiamen due to a significant increase in vegetation coverage, urban village redevelopment, and the construction of new parks; (4) sea ports and heavy industrial zones are the greatest contributor to surface UHI, followed by urban villages; and (5) LST is strongly positively correlated with ISA%. Each 10% increase in ISA was associated with an increase in summer LST of 0.41 to 0.91 K, which compares well with the results of related studies. This study presents valuable information for the development of regional urban planning strategies to mitigate the effects of UHIs during rapid urbanization.

Determination of application volume for coffee plantations using artificial neural networks and remote sensing

2021 ◽  
Vol 184 ◽  
pp. 106096
Author(s):  
Mailson Freire de Oliveira ◽  
Adão Felipe dos Santos ◽  
Elizabeth Haruna Kazama ◽  
Glauco de Souza Rolim ◽  
Rouverson Pereira da Silva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Coffee Plantations ◽  
Artificial Neural ◽  
Application Volume

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

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